Abstract
Factoid Question Answering (QA) is becoming an increasingly important research area in natural language processing. Since 1999, many international question answering contests have been held at conferences and workshops, such as TREC, CLEF, and NTCIR; and several languages have been tested in monolingual or cross-lingual question answering tasks. Although Chinese is growing in popularity worldwide, there seems to be a performance gap between Chinese question answering systems and some systems used for other languages. In this dissertation, our objective is to improve the performance of Chinese Factoid Question Answering systems. To this end, we investigate in the following two concepts. (1) Hybrid Modules Comprised of Knowledge-based and Machine Learning based Methods To date, most research on QA modules has focused on knowledge-based or machine learning based methods, possibly because hybrid methods are costly that both the knowledge-based and machine learning-based methods need to be adjusted, and it necessary to find an appropriate way to combine the methods in a hybrid model. To demonstrate the effect of hybrid modules, we developed a hybrid question classifier and used it to conduct a series of empirical experiments. Specifically, we compared the performances of the knowledge-based classifier, the machine learning based classifier and the hybrid classifier on several heterogeneous unseen questions from various sources. The results showed that the knowledge-based question classifier was more accurate than the machine learning-based classifier, but the proposed hybrid classifier achieved the highest accuracy. (2) Lightweight Question Answering Methods Nearly all the top performing systems use heavy methods that require sophisticated techniques, such as parsers or logic provers. However, such techniques are usually unavailable or unaffordable for under-resourced languages or in resource-limited situations. In contrast to state-of-the-art QA systems, we improve a top performing Chinese QA system by using lightweight methods effectively. We propose two lightweight methods, namely the Sum of Co-occurrences of Question and Answer Terms (SCO-QAT) and Alignment-based Surface Patterns (ABSPs). SCO-QAT is a co-occurrence-based answer ranking method that does not need extra knowledge, word-ignoring heuristic rules, or tools. It simply calculates co-occurrence scores based on the passage retrieval results. ABSPs are syntactic patterns trained from question-answer pairs with an alignment algorithm. They are used to capture the relations between terms; and the relations are used to filter answers. We attribute the success of the ABSP and SCO-QAT methods to the effective use of local syntactic information and global co-occurrence information. By using SCO-QAT and ABSPs, we improved the RU-Accuracy of our testbed QA system, ASQA, from 0.445 to 0.535 on the NTCIR-5 dataset. The system also achieved the top 0.5 RU-Accuracy on the NTCIR-6 dataset. The result shows that lightweight methods are not only less expensive to implement, but also have the potential to achieve state-of-the-art performances.